With the development of computation systems and image processing technologies, face recognition are becoming widely used in security systems, interactive video applications, image editing and archiving applications, and computer vision applications, etc. For example, video face recognition has been driven by its huge potential in developing applications in many domains including video surveillance security, augmented reality, automatic video tagging, medical analysis, quality control, and video-lecture assessment.
Face Recognition (FR) with illumination, block occlusion and disguise variations has been a research topic in computer vision. Current methods in FR suggest that frontal views of human faces can be represented as a linear combination of training-set faces. This representation can be used for classification or identification.
Simple regression models such as regularized least squares may provide reasonable performance when faces are clean and un-occluded. However, in more challenging cases (e.g., severe occlusion), robust regression models need to be utilized.
In face recognition methods based on robust statistics, error distribution between the test sample and the dictionary faces is implemented to be different from Gaussian and, thus, better representation vectors can be obtained. One approach is to detect outlier pixels (e.g., occluded pixels) in the face by regularized robust coding (RRC). In RRC, the residual term is described by a robust M-Estimator and obtained by solving an iterative reweighted least squares problem. Another approach is to iteratively correct distorted faces using robust statistics. Other options may include describing the residual by the robust correntropy induced metric, or use a nuclear norm regression model to describe the error distribution.
Improvements in face recognition rates may be achieved with block-based sparse representation based classification (SRC). The basic idea is to partition the face image into blocks and perform SRC identification by averaging the residuals from each block. Thus, more representation vectors are estimated to enhance identification performance. For example, in a relaxed collaborative representation (RCR) method, the similarity and distinctiveness of the representation vectors from each block is exploited by minimizing the variance among them. For another example, in a multi-task joint sparse representation based classification, multiple input features from the whole face are used instead of multi-block pixel intensities. However, these block-based representation methods detects similarities based on pixel intensities of the blocks, which may not fully describe characteristics of the blocks and thus are not very robust in various applications.
Further, accuracy and performance of existing methods in challenging face recognition cases still need to be improved. Therefore, it is desired to provide face recognition techniques for faces having frontal views with illumination, block occlusion and disguise variations. The disclosed method and system are directed to solve one or more problems set forth above and other problems.